Embodiments herein generally relate to methods and systems that diagnose printer defects and more particularly to systems and methods that provide the user with images of candidate defects that the user can use for comparison purposes to narrow or identify the defective component within the printer.
Failure in printers and copiers typically manifest themselves in defects seen on the printed image. Image quality defects typically account for more than 50% of system failures requiring service in the field and creating downtime for organizations running printers and copiers.
One common method often used to diagnose printer image quality defects is to evaluate standard image reference (SIR) images stored at the printer location. The primary goal when viewing the standard image references is to evaluate the severity of the defect. Additionally, the service agent or customer may scan through all standard image references created for the printing system to help diagnose and isolate the defective component.
A dynamic diagnostic image as described in this disclosure provides the customer the results from a diagnostic inference engine and a visual verification of the current defect compared to a library of defects for the known failure modes in a printing system. The embodiments herein utilize customer or service agent input of the defect description, the current machine health, and knowledge from a system diagnostic design and inference engine, and display the example defects at the phase of life as an image on the printer's display screen. The diagnostic image allows for visual verification of diagnostic inference engine or possible final component ambiguity resolution. Finally, the diagnostic image enables a semi-automatic diagnostic plan in the absence of the ideal automatic diagnostic system with zero percent error.
One exemplary method embodiment herein receives printing symptoms from a user into a graphic user interface and receives system information from a printing device exhibiting the printing symptoms. The method analyzes the printing symptoms using a diagnostic inference system operating on a computerized device operatively connected to the graphic user interface to produce candidate component defects. The method outputs diagnostic recommendations containing the candidate component defects to the user. The diagnostic recommendations include at least one representative image of a printing defect corresponding to each candidate component, and probabilities of correctness of the candidate component defects displayed alongside the representative image.
This output of diagnostic recommendations can comprise component replacement, repair, adjustment, etc. Alternatively, the methods herein can loop back through the process and display at least one additional image of at least one additional printing defect using the graphic user interface and receive additional user input regarding similarities between the additional images of the additional printing defects and the printing marks. Further, with some embodiments herein, the analysis performed can produce probabilities of correctness of the candidate component defects, and such probabilities can be displayed alongside the images on the graphic user interface.
In addition, portions herein also include apparatus embodiments. One such exemplary apparatus embodiment includes a computerized device, a graphic user interface operatively connected to (directly or indirectly connected to) the computerized device, and a printing device exhibiting printing symptoms. The graphic user interface receives input of the printing symptoms from a user, and the computerized device receives system information from the printing device.
The computerized device analyzes the printing symptoms and the system information to produce candidate component defects. The computerized device outputs diagnostic recommendations containing the candidate component defects to the user, the diagnostic recommendations include at least one representative image of a printing defect corresponding to each candidate component, and the diagnostic recommendations include probabilities of correctness of the candidate component defects displayed alongside the representative image. The images of the candidate component defects are compared to printing marks on the diagnostic page by the user to confirm the diagnostic recommendations.
These and other features are described in, or are apparent from, the following detailed description.